Update app.py

app.py

@@ -1,30 +1,34 @@
-import scripts.control_utils as cu
+from diffusers import AutoencoderKL, StableDiffusionXLControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
 import torch
-from PIL import Image
+from controlnet_aux import OpenposeDetector
+from diffusers.utils import load_image
 
-path_to_config = 'ControlNet-XS-main/configs/inference/sdxl/sdxl_encD_canny_48m.yaml'
-model = cu.create_model(path_to_config).to('cuda')
 
-image_path = 'PATH/TO/IMAGES/Shoe.png'
+# Compute openpose conditioning image.
+openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
 
-canny_high_th = 250
-canny_low_th = 100
-size = 768
-num_samples=2
-
-image = cu.get_image(image_path, size=size)
-edges = cu.get_canny_edges(image, low_th=canny_low_th, high_th=canny_high_th)
-
-samples, controls = cu.get_sdxl_sample(
-    guidance=edges,
-    ddim_steps=10,
-    num_samples=num_samples,
-    model=model,
-    shape=[4, size // 8, size // 8],
-    control_scale=0.95,
-    prompt='cinematic, shoe in the streets, made from meat, photorealistic shoe, highly detailed',
-    n_prompt='lowres, bad anatomy, worst quality, low quality',
+image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
 )
+openpose_image = openpose(image)
 
+# Initialize ControlNet pipeline.
+controlnet = ControlNetModel.from_pretrained("thibaud/controlnet-openpose-sdxl-1.0")
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet
+)
+#pipe.enable_model_cpu_offload()
 
-Image.fromarray(cu.create_image_grid(samples)).save('SDXL_MyShoe.png')
+def pose_calc():
+    # Infer.
+    prompt = "Darth vader dancing in a desert, high quality"
+    negative_prompt = "low quality, bad quality"
+    images = pipe(
+        prompt, 
+        negative_prompt=negative_prompt,
+        num_inference_steps=25,
+        num_images_per_prompt=4,
+        image=openpose_image.resize((1024, 1024)),
+        generator=torch.manual_seed(97),
+    ).images
+    return images[0]